Identification Of Essential Genes Research Articles

An RNA Interference Tool to Silence Genes in Sarcoptes scabiei Eggs.

In a quest for new interventions against scabies—a highly significant skin disease of mammals, caused by a parasitic mite Sarcoptes scabiei—we are focusing on finding new intervention targets. RNA interference (RNAi) could be an efficient functional genomics approach to identify such targets. The RNAi pathway is present in S. scabiei and operational in the female adult mite, but other developmental stages have not been assessed. Identifying potential intervention targets in the egg stage is particularly important because current treatments do not kill this latter stage. Here, we established an RNAi tool to silence single-copy genes in S. scabiei eggs. Using sodium hypochlorite pre-treatment, we succeeded in rendering the eggshell permeable to dsRNA without affecting larval hatching. We optimised the treatment of eggs with gene-specific dsRNAs to three single-copy target genes (designated Ss-Cof, Ss-Ddp, and Ss-Nan) which significantly and repeatedly suppressed transcription by ~66.6%, 74.3%, and 84.1%, respectively. Although no phenotypic alterations were detected in dsRNA-treated eggs for Ss-Cof and Ss-Nan, the silencing of Ss-Ddp resulted in a 38% reduction of larval hatching. This RNAi method is expected to provide a useful tool for larger-scale functional genomic investigations for the identification of essential genes as potential drug targets.

Prediction of gene essentiality using machine learning and genome-scale metabolic models

The identification of essential genes, i.e. those that impair cell survival when deleted, requires large growth assays of knock-out strains. The complexity and cost of such experiments has triggered a growing interest in computational methods for prediction of gene essentiality. In the case of metabolic genes, Flux Balance Analysis (FBA) is widely employed to predict essentiality under the assumption that cells maximize their growth rate. However, this approach assumes that knock-out strains optimize the same objectives as the wild-type, which excludes cases in which deletions cause large physiological changes to meet other objectives for survival. Here, we resolve this limitation with a novel machine learning approach that predicts essentiality directly from wild-type flux distributions. We first project the wild-type FBA solution onto a mass flow graph, a digraph with reactions as nodes and edge weights proportional to the mass transfer between reactions, and then train binary classifiers on the connectivity of graph nodes. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli, achieving near state-of-the art prediction accuracy for essential genes. Our approach suggests that wild-type FBA solutions contain enough information to predict essentiality, without the need to assume optimality of deletion strains.

Identification of essential genes involved in metabolism-based resistance mechanism to fenoxaprop-P-ethyl in Polypogon fugax.

Metabolic resistance is a worldwide concern for weed control but has not yet been well-characterized at the genetic level. Previously, we have identified an Asia minor bluegrass (Polypogon fugax Nees ex Steud.) population AHHY exhibiting cytochrome P450 (P450)-involved metabolic resistance to fenoxaprop-P-ethyl. In this study, we aimed to confirm the metabolic fenoxaprop-P-ethyl resistance in AHHY and uncover the potential herbicide metabolism-related genes in this economically damaging weed species. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) assays indicated the metabolic rates of fenoxaprop-P-ethyl were significantly faster in resistant (R, AHHY) than in susceptible (S, SDTS) plants. The amount of phytotoxic fenoxaprop-P peaked at 12 h after herbicide treatment (HAT) and started to decrease at 24 HAT in both biotypes. R and S plants at 24 HAT were sampled to conduct isoform-sequencing (Iso-Seq) and RNA-sequencing (RNA-Seq). A reference transcriptome containing 24 972 full-length isoforms was obtained, of which 24 329 unigenes were successfully annotated. Transcriptomic profiling identified 28 detoxifying enzyme genes constitutively and/or herbicide-induced up-regulated in R than in S plants. Real-time quantitative polymerase chain reaction (RT-qPCR) confirmed 17 genes were consistently up-regulated in R and its F1 generation plants. They were selected as potential fenoxaprop-P-ethyl metabolism-related genes, including ten P450s, one glutathione-S-transferase, one UDP-glucosyltransferase, and five adenosine triphosphate (ATP)-binding cassette transporters. This study revealed that the enhanced rates of fenoxaprop-P-ethyl metabolism in P. fugax were very likely driven by the herbicide metabolism-related genes. The transcriptome data generated by Iso-Seq combined with RNA-Seq will provide abundant gene resources for understanding the molecular mechanisms of resistance in P. fugax.

Identification of Essential Genes in Caenorhabditis elegans with Lethal Mutations Maintained by Genetic Balancers.

Genetic balancer systems, which allow effective capture and maintenance of lethal mutations stably, play an important role in identifying essential genes. Whole-genome sequencing (WGS) followed by bioinformatics analysis, combined with genetic mapping data analysis, allows for an efficient and economical means of identifying genomic mutations in essential genes. Using this approach, we successfully identified 104 essential genes on ChrI, ChrIII, and ChrV in C. elegans. In this report, we described a protocol that sequences the genome of prebalanced Caenorhabditis elegans (C. elegans) strains to carry lethal mutations and identifies candidate causal mutations and candidate essential genes using a robust bioinformatics procedure.

Identification of Essential Genes Using Sequential CRISPR and siRNA Screens.

Genome-wide CRISPR and siRNA screening methodologies are powerful tools that are aptly suited to the discovery of essential genes. In this chapter, we outline our methods to conduct sequential CRISPR and siRNA screens to quickly and efficiently identify essential genes within a collection of cell lines. The utilization of both screening methodologies provides a pipeline that minimizes costs and time while enabling the robust detection of candidate genes.

The Application of Transposon Insertion Sequencing in Identifying Essential Genes in B. fragilis.

Essential genes are those that are indispensable for the survival of organism under specific growth conditions. Investigating essential genes in pathogenic bacteria not only helps to understand vital biological networks but also provides novel targets for drug development. Availability of genetic engineering tools and high-throughput sequencing methods has enabled essential genes identification in many pathogenic gram-positive and gram-negative bacteria. Bacteroides fragilis is one of the major bacteria specific of human gastrointestinal microbiota. When B. fragilis moves out of its niche, it turns into deadly pathogen. Here, we describe detailed method for the essential gene identification in B. fragilis. Generated transposon mutant pool can be used for other applications such as identification of genes responsible for drug resistance in B. fragilis.

Using Genome Scale Mutant Libraries to Identify Essential Genes.

Identification of essential genes is key to understanding the required processes and gene products of organisms under one or more conditions. Transposon sequencing (Tn-seq) has been used to predict essential genes or ones that conditionally impact fitness in a wide variety of organisms. Here, we describe the generation of genome-scale mutant libraries and the analysis of Tn-seq data to identify essential genes from cultures grown in a single condition as well as those that are conditionally important by analyzing the behavior of these mutant libraries in different growth environments. While we illustrate the approach using data derived from Tn-seq analysis of the α-proteobacteria Rhodobacter sphaeroides and Zymomonas mobilis, the protocols and systems we describe should be generally applicable to a variety of organisms.

Recent Advances in Molecular Genetic Tools for Babesia.

Development of in vitro culture and completion of genome sequencing of several Babesia parasites promoted the efforts to establish transfection systems for these parasites to dissect the gene functions. It has been more than a decade since the establishment of first transfection for Babesia bovis, the causative agent of bovine babesiosis. However, the number of genes that were targeted by genetic tools in Babesia parasites is limited. This is partially due to the low efficiencies of these methods. The recent adaptation of CRISPR/Cas9 for genome editing of Babesia bovis can accelerate the efforts for dissecting this parasite’s genome and extend the knowledge on biological aspects of erythrocytic and tick stages of Babesia. Additionally, glmS ribozyme as a conditional knockdown system is available that could be used for the characterization of essential genes. The development of high throughput genetic tools is needed to dissect the function of multigene families, targeting several genes in a specific pathway, and finally genome-wide identification of essential genes to find novel drug targets. In this review, we summarized the current tools that are available for Babesia and the genes that are being targeted by these tools. This may draw a perspective for the future development of genetic tools and pave the way for the identification of novel drugs or vaccine targets.

FunOrder: A robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution.

Secondary metabolites (SMs) are a vast group of compounds with different structures and properties that have been utilized as drugs, food additives, dyes, and as monomers for novel plastics. In many cases, the biosynthesis of SMs is catalysed by enzymes whose corresponding genes are co-localized in the genome in biosynthetic gene clusters (BGCs). Notably, BGCs may contain so-called gap genes, that are not involved in the biosynthesis of the SM. Current genome mining tools can identify BGCs, but they have problems with distinguishing essential genes from gap genes. This can and must be done by expensive, laborious, and time-consuming comparative genomic approaches or transcriptome analyses. In this study, we developed a method that allows semi-automated identification of essential genes in a BGC based on co-evolution analysis. To this end, the protein sequences of a BGC are blasted against a suitable proteome database. For each protein, a phylogenetic tree is created. The trees are compared by treeKO to detect co-evolution. The results of this comparison are visualized in different output formats, which are compared visually. Our results suggest that co-evolution is commonly occurring within BGCs, albeit not all, and that especially those genes that encode for enzymes of the biosynthetic pathway are co-evolutionary linked and can be identified with FunOrder. In light of the growing number of genomic data available, this will contribute to the studies of BGCs in native hosts and facilitate heterologous expression in other organisms with the aim of the discovery of novel SMs.

Identification of essential genes in Caenorhabditis elegans through whole-genome sequencing of legacy mutant collections.

It has been estimated that 15%–30% of the ∼20,000 genes in C. elegans are essential, yet many of these genes remain to be identified or characterized. With the goal of identifying unknown essential genes, we performed whole-genome sequencing on complementation pairs from legacy collections of maternal-effect lethal and sterile mutants. This approach uncovered maternal genes required for embryonic development and genes with apparent sperm-specific functions. In total, 58 putative essential genes were identified on chromosomes III–V, of which 52 genes are represented by novel alleles in this collection. Of these 52 genes, 19 (40 alleles) were selected for further functional characterization. The terminal phenotypes of embryos were examined, revealing defects in cell division, morphogenesis, and osmotic integrity of the eggshell. Mating assays with wild-type males revealed previously unknown male-expressed genes required for fertilization and embryonic development. The result of this study is a catalog of mutant alleles in essential genes that will serve as a resource to guide further study toward a more complete understanding of this important model organism. As many genes and developmental pathways in C. elegans are conserved and essential genes are often linked to human disease, uncovering the function of these genes may also provide insight to further our understanding of human biology.

Targeted genetic screening in bacteria with a Cas12k-guided transposase.

Microbes employ sophisticated cellular networks encoded by complex genomes to rapidly adapt to changing environments. High-throughput genome engineering methods are valuable tools for functionally profiling genotype-phenotype relationships and understanding the complexity of cellular networks. However, current methods either rely on special homologous recombination systems and are thus applicable in only limited bacterial species or can generate only nonspecific mutations and thus require extensive subsequent screening. Here, we report a site-specific transposon-assisted genome engineering (STAGE) method that allows high-throughput Cas12k-guided mutagenesis in various microorganisms, such as Pseudomonas aeruginosa and Klebsiella pneumoniae. Exploiting the powerful STAGE technique, we construct a site-specific transposon mutant library that focuses on all possible transcription factors (TFs) in P.aeruginosa, enabling the comprehensive identification of essential genes and antibiotic-resistance-related factors. Given its broad host range activity and easy programmability, this method can be widely adapted to diverse microbial species for rapid genome engineering and strain evolution.

Predicting essential genes of 37 prokaryotes by combining information-theoretic features

Essential genes are required for the reproduction and survival of an organism. Rapid identification of essential genes has practical application value in biomedicine. Information theory is a discipline that studies information transmission. Based on the similarity between heredity and information transmission, measures derived from information theory can be applied to genetic sequence analysis on different scales.In this study, we employed 114 features extracted by information theory methods to construct an essential gene prediction model. We applied a backpropagation neural network to construct a classifier and employed it to predict essential genes of 37 prokaryotes. The performance of the classifier was evaluated by applying intra-organism prediction and leave-one-species-out prediction. Among 37 prokaryotes, intra-organism prediction and leave-one-species-out prediction yielded average AUC scores of 0.791 and 0.717, respectively. Considering the potential redundancy in the feature set, we performed feature selection and constructed a key feature subset. In the above two prediction methods, the average AUC scores of 37 organisms obtained by using key features were 0.786 and 0.714, respectively. The results show the potential and universality of information-theoretic features in the study of prokaryotic essential gene prediction.

Machine learning approach to gene essentiality prediction: a review.

Identification of essential genes is imperative because it provides an understanding of the core structure and function, accelerating drug targets' discovery, among other functions. Recent studies have applied machine learning to complement the experimental identification of essential genes. However, several factors are limiting the performance of machine learning approaches. This review aims to present the standard procedure and resources available for predicting essential genes in organisms, and also highlight the factors responsible for the current limitation in using machine learning for conditional gene essentiality prediction. The choice of features and ML technique was identified as an important factor to predict essential genes effectively.

Multi-Omics Data Fusion via a Joint Kernel Learning Model for Cancer Subtype Discovery and Essential Gene Identification.

The multiple sources of cancer determine its multiple causes, and the same cancer can be composed of many different subtypes. Identification of cancer subtypes is a key part of personalized cancer treatment and provides an important reference for clinical diagnosis and treatment. Some studies have shown that there are significant differences in the genetic and epigenetic profiles among different cancer subtypes during carcinogenesis and development. In this study, we first collect seven cancer datasets from the Broad Institute GDAC Firehose, including gene expression profile, isoform expression profile, DNA methylation expression data, and survival information correspondingly. Furthermore, we employ kernel principal component analysis (PCA) to extract features for each expression profile, convert them into three similarity kernel matrices by Gaussian kernel function, and then fuse these matrices as a global kernel matrix. Finally, we apply it to spectral clustering algorithm to get the clustering results of different cancer subtypes. In the experimental results, besides using the P-value from the Cox regression model and survival analysis as the primary evaluation measures, we also introduce statistical indicators such as Rand index (RI) and adjusted RI (ARI) to verify the performance of clustering. Then combining with gene expression profile, we obtain the differential expression of genes among different subtypes by gene set enrichment analysis. For lung cancer, GMPS, EPHA10, C10orf54, and MAGEA6 are highly expressed in different subtypes; for liver cancer, CMYA5, DEPDC6, FAU, VPS24, RCBTB2, LOC100133469, and SLC35B4 are significantly expressed in different subtypes.

Gene Switching and Essentiality Testing.

The identification of essential genes is of major importance to mycobacterial research, and a number of essential genes have been identified in mycobacteria, however confirming essentiality is not straightforward, as deletion of essential genes results in a lethal phenotype. In this chapter, protocols are described which can be used to confirm gene essentiality using gene switching, following the construction of a strain carrying its only functional copy on an integrated plasmid (Δ'int). Since deletion mutants cannot be created for essential genes, a second gene copy is introduced via an integrating vector, which allows the chromosomal gene copy to be deleted. The integrated vector can then be replaced using the gene switching method, where no transformants are obtained, essentiality is confirmed. This technique can also be used to confirm functionality of gene homologs and to easily identify essential operon members.

Identification of Drug Resistance Genes Using a Pooled Lentiviral CRISPR/Cas9 Screening Approach.

In addition to advancing the development of gene-editing therapeutics, CRISPR/Cas9 is transforming how functional genetic studies are carried out in the lab. By increasing the ease with which genetic information can be inserted, deleted, or edited in cell and organism models, it facilitates genotype-phenotype analysis. Moreover, CRISPR/Cas9 has revolutionized the speed at which new genes underlying a particular phenotype can be identified through its application in genomic screens. Arrayed high-throughput and pooled lentiviral-based CRISPR/Cas9 screens have now been used in a wide variety of contexts, including the identification of essential genes, genes involved in cancer metastasis and tumor growth, and even genes involved in viral response. This technology has also been successfully used to identify drug targets and drug resistance mechanisms. Here, we provide a detailed protocol for performing a genome-wide pooled lentiviral CRISPR/Cas9 knockout screen to identify genetic modulators of a small-molecule drug. While we exemplify how to identify genes involved in resistance to a cytotoxic histone deacetylase inhibitor, Trichostatin A (TSA), the workflow we present can easily be adapted to different types of selections and other types of exogenous ligands or drugs.

Identification of Essential Genes and Fluconazole Susceptibility Genes in Candida glabrata by Profiling Hermes Transposon Insertions.

Within the budding yeasts, the opportunistic pathogen Candida glabrata and other members of the Nakaseomyces clade have developed virulence traits independently from C. albicans and C. auris. To begin exploring the genetic basis of C. glabrata virulence and its innate resistance to antifungals, we launched the Hermes transposon from a plasmid and sequenced more than 500,000 different semi-random insertions throughout the genome. With machine learning, we identified 1278 protein-encoding genes (25% of total) that could not tolerate transposon insertions and are likely essential for C. glabrata fitness in vitro. Interestingly, genes involved in mRNA splicing were less likely to be essential in C. glabrata than their orthologs in S. cerevisiae, whereas the opposite is true for genes involved in kinetochore function and chromosome segregation. When a pool of insertion mutants was challenged with the first-line antifungal fluconazole, insertions in several known resistance genes (e.g., PDR1, CDR1, PDR16, PDR17, UPC2A, DAP1, STV1) and 15 additional genes (including KGD1, KGD2, YHR045W) became hypersensitive to fluconazole. Insertions in 200 other genes conferred significant resistance to fluconazole, two-thirds of which function in mitochondria and likely down-regulate Pdr1 expression or function. Knockout mutants of KGD2 and IDH2, which consume and generate alpha-ketoglutarate in mitochondria, exhibited increased and decreased resistance to fluconazole through a process that depended on Pdr1. These findings establish the utility of transposon insertion profiling in forward genetic investigations of this important pathogen of humans.

A CRISPR-based method for testing the essentiality of a gene

The CRISPR/Cas9 system is a powerful method of editing genes by randomly introducing errors into the target sites. Here, we describe a CRISPR-based test for gene essentiality (CRISPR-E test) that allows the identification of essential genes. Specifically, we use sgRNA-mediated CRISPR/Cas9 to target the open reading frame of a gene in the genome and analyze the in-frame (3n) and frameshift (3n + 1 and 3n + 2) mutations in the targeted region of the gene in surviving cells. If the gene is non-essential, the cells would carry both in-frame (3n) and frameshift (3n + 1 and 3n + 2) mutations. In contrast, the cells would carry only in-frame (3n) mutations if the targeted gene is essential, and this selective elimination of frameshift (3n + 1 and 3n + 2) mutations of the gene indicate its essentiality. As a proof of concept, we have used this CRISPR-E test in the model organism Dictyostelium discoideum to demonstrate that Dync1li1 is an essential gene while KIF1A and fAR1 are not. We further propose a simple method for quantifying the essentiality of a gene using the CRISPR-E test.

Expression-based prediction of human essential genes and candidate lncRNAs in cancer cells.

Essential genes are required for the reproductive success at either cellular or organismal level. The identification of essential genes is important for understanding the core biological processes and identifying effective therapeutic drug targets. However, experimental identification of essential genes is costly, time consuming and labor intensive. Although several machine learning models have been developed to predict essential genes, these models are not readily applicable to lncRNAs. Moreover, the currently available models cannot be used to predict essential genes in a specific cancer type. In this study, we have developed a new machine learning approach, XGEP (eXpression-based Gene Essentiality Prediction), to predict essential genes and candidate lncRNAs in cancer cells. The novelty of XGEP lies in the utilization of relevant features derived from the TCGA transcriptome dataset through collaborative embedding. When evaluated on the pan-cancer dataset, XGEP was able to accurately predict human essential genes and achieve significantly higher performance than previous models. Notably, several candidate lncRNAs selected by XGEP are reported to promote cell proliferation and inhibit cell apoptosis. Moreover, XGEP also demonstrated superior performance on cancer-type-specific datasets to identify essential genes. The comprehensive lists of candidate essential genes in specific cancer types may be used to guide experimental characterization and facilitate the discovery of drug targets for cancer therapy. The source code and datasets used in this study are freely available at https://github.com/BioDataLearning/XGEP. Supplementary data are available at Bioinformatics online.

A Highly Sensitive, Accurate, and Automated Single-Cell RNA Sequencing Platform with Digital Microfluidics

Single-cell RNA sequencing (scRNA-seq) is a powerful method in investigating single-cell heterogeneity to reveal rare cells, identify cell subpopulations, and construct a cell atlas. Conventional benchtop methods for scRNA-seq, including multistep operations, are labor intensive, reaction inefficient, contamination prone, and reagent consuming. Here we report a digital microfluidics-based single-cell RNA sequencing (digital-RNA-seq) for simple, efficient, and low-cost single-cell mRNA measurements. Digital-RNA-seq automates fluid handling as discrete droplets to sequentially perform protocols of scRNA-seq. To overcome the current problems of single-cell isolation in efficiency, integrity, selectivity, and flexibility, we propose a new strategy, passive dispensing method, relying on well-designed hydrophilic-hydrophobic microfeatures to rapidly generate single-cell subdroplets when a droplet of cell suspension is encountered. For sufficient cDNA generation and amplification, digital-RNA-seq uses nanoliter reaction volumes and hydrophobic reaction interfaces, achieving high sensitivity in gene detection. Additionally, the stable droplet handling and oil-closed reaction space featured in digital-RNA-seq ensure highly accurate measurement. We demonstrate the functionality of digital-RNA-seq by quantifying heterogeneity among single cells, where digital-RNA-seq shows excellent performance in rare transcript detection, cell type differentiation, and essential gene identification. With the advantages of automation, sensitivity, and accuracy, digital-RNA-seq represents a promising scRNA-seq platform for a wide variety of biological applications.